Multimodal Fusion Convolutional Neural Network With Cross-Attention Mechanism for Internal Defect Detection of Magnetic Tile

Lu, Houhong; Zhu, Yangyang; Yin, Ming; Yin, Guofu; Xie, Luofeng

doi:10.1109/access.2022.3180725

Cited by 62 publications

(20 citation statements)

References 30 publications

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“…Since the main concept of the forebody is to reduce drag reduction, a new methodology should consider this for thermal management of aerodynamic heating. In fact, the reduction of the both heat and drag should be balanced for the efficient model 6 , 7 . The mechanical device of spike is the most conventional practical model for the thermal reduction of the nose cone at hypersonic flow.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of compressible flow around nose cone with Multi-row disk and multi coolant jets

Iranmanesh

Alizadeh

Faraji

et al. 2023

Sci Rep

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Due to sever aerodynamic heating, the protection of forebody of scramjet is crucial for hypersonic flight. In present work, a new cooling system is proposed and investigated for the protection of nose cone at hypersonic flight. Computational fluid dynamic is used for the simulation of the lateral and axial coolant jet released from the spike at high-velocity condition. The primary goal is to find optimum jet location for efficient cooling of nose and spike assembly. Influence of two coolant jets (Carbon dioxide and Helium) on the mechanism of cooling system are fully investigated. For simulation, RANS equations are coupled with species transport equation and SST turbulence model. Two different jet configurations (axial disk positions) are investigated to obtain efficient condition for protection of nose cone at hypersonic flight. Our results indicate that the presence of the spike on the nose cone decreases pressure up to 33% on the main body and the shifts the maximum pressure to higher angles because of the deflection of the air stream. Maximum pressure drops about 50% by injection of the coolant disk jet (C2) at angle of 55 deg.

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Section: Introductionmentioning

confidence: 99%

Numerical investigation of compressible flow around nose cone with Multi-row disk and multi coolant jets

Iranmanesh

Alizadeh

Faraji

et al. 2023

Sci Rep

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show abstract

“…Our investigation substantially enhances existing methods in tile defect detection, as evidenced by a comprehensive comparison with prior studies [5][6][7][8][9][10][11][12][13][14] delineated in table 3. Our approach introduces a holistic improvement in several key performance indicators, warranting a detailed discussion on each advancement.…”

Section: Comparative Analysis In Tile Defect Detectionmentioning

confidence: 69%

“…Acknowledging the limitations of current methodologies, such as the need for multiple devices and the high costs associated with AI-based diagnostics [7,8,13], the research community has explored the potential of tiny machine learning (TinyML), a technology that combines machine learning with microcontroller units (MCUs) [33][34][35][36][37]. TinyML optimizes models for devices with limited computational resources, enabling the deployment of machine-learning models on a smaller scale.…”

Section: Introductionmentioning

confidence: 99%

Real-time hollow defect detection in tiles using on-device tiny machine learning

Lin,

Chang,

Zhuang

et al. 2024

Meas. Sci. Technol.

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This study addresses the challenge of subsurface defect detection in floor tiles for quality control in residential construction. To overcome the limitations of traditional inspection methods and the complexities associated with existing artificial intelligence (AI)-based approaches, we have developed the AI Diagnostic Stick (AID-Stick), a novel tool designed to advance the field of tile defect detection. This innovative tool integrates an embedded machine-learning framework, leveraging convolutional neural networks (CNNs) and tiny machine learning (TinyML) techniques. The AID-Stick utilizes spectrogram, Mel-frequency cepstral coefficient (MFCC), and Mel filterbank energy (MFE) for real-time, on-microcontroller unit (MCU) diagnostics of auditory signals from tile tapping tests. Our methodology effectively utilizes these acoustic features in distinguishing between intact and subsurface hollow defective tiles. The study's findings, revealing a notable validation accuracy of 97% and a real-world accuracy of 81.25%, showcase a promising improvement over traditional methods. The AID-Stick's practicality, cost-effectiveness, and user-friendly design make it potentially beneficial for small-to-medium enterprises and economically constrained markets. Furthermore, this research opens avenues for future enhancements in embedded AI systems, with potential applications extending beyond the construction industry to other domains requiring non-destructive testing. This work not only contributes to the field of industrial quality control but also to the development of intelligent diagnostic tools, paving the way for future innovations in automated inspection technologies.

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“…Some researchers brought them into finger-vein recognition. For example, deep learning approaches are employed for vein image segmentation (Liskowski and Krawiec, 2016 ; Qin et al, 2019 ; Yang et al, 2019 ; Shaheed et al, 2022a ), quality assessment of vein image (Qin and Yacoubi, 2015 ; Qin and El-Yacoubi, 2018 ), fuzzy networks (Liu H. et al, 2022 ; Lu et al, 2022 ; Muthusamy and Rakkimuthu, 2022 ), and finger-vein recognition (Wang et al, 2017 ; Avci et al, 2019 ; Gumusbas et al, 2019 ; Zhang J. et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

A deep ensemble learning method for single finger-vein identification

Liu

Qin²,

Song³

et al. 2023

Front. Neurorobot.

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Finger-vein biometrics has been extensively investigated for personal verification. Single sample per person (SSPP) finger-vein recognition is one of the open issues in finger-vein recognition. Despite recent advances in deep neural networks for finger-vein recognition, current approaches depend on a large number of training data. However, they lack the robustness of extracting robust and discriminative finger-vein features from a single training image sample. A deep ensemble learning method is proposed to solve the SSPP finger-vein recognition in this article. In the proposed method, multiple feature maps were generated from an input finger-vein image, based on various independent deep learning-based classifiers. A shared learning scheme is investigated among classifiers to improve their feature representation captivity. The learning speed of weak classifiers is also adjusted to achieve the simultaneously best performance. A deep learning model is proposed by an ensemble of all these adjusted classifiers. The proposed method is tested with two public finger vein databases. The result shows that the proposed approach has a distinct advantage over all the other tested popular solutions for the SSPP problem.

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Multimodal Fusion Convolutional Neural Network With Cross-Attention Mechanism for Internal Defect Detection of Magnetic Tile

Cited by 62 publications

References 30 publications

Numerical investigation of compressible flow around nose cone with Multi-row disk and multi coolant jets

Numerical investigation of compressible flow around nose cone with Multi-row disk and multi coolant jets

Real-time hollow defect detection in tiles using on-device tiny machine learning

A deep ensemble learning method for single finger-vein identification

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